Psychon Bull Rev
DOI 10.3758/s13423-011-0109-6
BRIEF REPORT
Catching the mind in flight: Using behavioral indices
to detect mindless reading in real time
Michael S. Franklin & Jonathan Smallwood &
Jonathan W. Schooler
# Psychonomic Society, Inc. 2011
It is no surprise that when one’s attention is diverted away
from a particular task, these lapses of attention, or mindwandering episodes, are often accompanied by measureable
changes in performance. Although there has been a good
deal of research documenting these effects in the laboratory
using sustained attention tasks (McVay & Kane, 2009;
Smallwood et al. 2004), the common experience of mind
wandering while reading (i.e., having one’s eyes continue
to move across a page of text, while the mind is focused
elsewhere) has until recently received little attention.
Fortunately, since considerable research has been done to
identify the critical processes necessary for successful text
comprehension, such as phonological and lexical processing (e.g., Perfetti et al. 2007), working memory (see, e.g.,
Daneman & Carpenter, 1980), and metacognitive skills
(e.g., Brown & Palincsar, 1987), these findings can be
exploited to discover how mind wandering impacts upon
these skills. The aim of the present study is to use these
systematic differences between mindful versus mindless
reading to predict off-task reading before it is reported by a
participant.
In the studies that have investigated mindless reading,
participants typically read text and are periodically probed
by asking whether at that particular moment their minds are
on or off task, followed by a reading comprehension test
M. S. Franklin (*) : J. W. Schooler
Department of Psychology, University of California,
Bldg 551, Room 1304,
Santa Barbara, CA 93106–9660, USA
e-mail: [email protected]
J. Smallwood
Department of Social Neuroscience,
Max Planck Institute for Brain & Cognition,
Leipzig, Germany
(Schooler, Reichle, & Halpern, 2004; Smallwood, McSpadden, & Schooler, 2008). The results from these studies have
revealed that the frequency of mindless reading is highly
correlated with reading comprehension performance, which
indicates the important relationship between mind wandering and comprehension failure (Smallwood, Fishman, &
Schooler, 2007).
In addition to studying how mind wandering influences
overall text comprehension, researchers have also begun to
focus on how mindless reading influences the processing of
lexical features of the words being read. When individuals
read, there is typically a strong relationship between the
lexical properties of the words and the amount of time that
is devoted to their processing (Rayner, 1998). Reichle,
Reineberg, and Schooler (2010) measured eye movements
during reading and showed that while gaze durations prior
to on-task reports were sensitive to lexical features, such as
word length and word frequency, these effects were
attenuated in periods immediately prior to off-task reports.
In addition, a recent study by Smilek, Carriere, and Cheyne
(2010) showed that mindless reading is associated with
increased blinking. This result could help explain the deficit
in encoding the lexical features of words, since frequent
blinking is associated with deactivation of cortical areas
that process the external visual world (Bristow, Haynes,
Sylvester, Frith, & Rees, 2005). Consistent with these
findings are results indicating that reading comprehension
can be compromised when critical regions of the text are
poorly encoded (Christianson, Williams, Zacks, & Ferreira,
2006; Sanford & Graesser, 2006; Stine-Morrow, Noh, &
Shake, 2010). Together, these findings indicate that the
negative impact of mindless reading is likely a consequence
of participants’ neglect of the visual, phonological, and
semantic features of the words, which has been described as
the “cascade model of inattention” (Smallwood, 2011).
Psychon Bull Rev
Present study
The strong relationship between reading times and mind
wandering suggests that it may be possible to detect
mindless reading by discerning when reading behavior
deviates from what can be considered normal. Here, we
attempted to use the speed with which participants
manually advanced words in a word-by-word reading
paradigm to predict, prior to their being probed, whether
participants would report mind wandering. The word-byword paradigm has the notable advantage over costly
eyetracking devices of working on any computer, while
nevertheless closely mirroring the behavioral reading
patterns observed with eyetracking (Just, Carpenter, &
Woolley, 1982). Thus, identification of a behavioral
signature of mind wandering with this paradigm has the
potential to provide a widely applicable methodology for
discerning mindless reading.
Importantly, pilot testing confirmed that participants’
behavior in a word-by-word reading paradigm varied based
on whether or not they reported mind wandering. 1
Consistent with Reichle et al. (2010), we found a number
of lexical effects that were attenuated when participants
reported being off task. Specifically, it was shown that
when on task, participants become significantly slower for
many-letter, multisyllable, low-familiarity words. When off
task, participants no longer slow to the same extent for
these words. Interestingly, however, whereas Reichle et al.
reported increased gaze durations during periods of mind
wandering, the present findings showed that overall,
participants tended to go faster when they were off task.
One potential reason for this discrepancy between studies
might be the different task environments; whereas, in our
study, the words were displayed individually at the center
of the screen and required a manual response to proceed to
the next word, Reichle et al. had participants read their text
one page at a time (see the General Discussion for further
elaboration on this point).
The aim of the present study was to use differences in
the patterns of reaction times associated with attentive and
inattentive reading to create an algorithm that can predict in
real time when participants are reading mindlessly. The
online identification of mindless reading based on real-time
appraisals of participants’ reaction times would offer a key
1
These findings are based on analyses performed on an additional set
of participants (N = 29), run using the same methods used in
Smallwood, McSpadden, and Schooler (2008). In order to investigate
the effects of mind wandering on reading, we analyzed reaction times
using a time window that extended 60 words (corresponding to
approximately 20–30 s) prior to the thought probe. The lexical
category assignment (word length, number of syllables, familiarity)
was the same as is described in the Method section.
advance toward the development of a pedagogical tool for
minimizing the negative impact of mindless reading on
reading comprehension. In addition, by running participants
without thought probes, we hoped to show that the
predicted number of mind-wandering episodes correlated
negatively with reading comprehension. This would suggest that the algorithm could be used to covertly track mind
wandering, and could therefore be a powerful tool for
investigating the processes involved in mind wandering,
without requiring participants to explicitly report their
mental states.
Method
Participants
A total of 49 participants from the University of California,
Santa Barbara, were tested in the experiment (23 female, 26
male; mean age = 19.2 years). Of these participants, 28
performed the task with thought probes, and 21 received no
thought probes.
Materials
Text The text used in this experiment was a shortened
version of “The Red-Headed League” (Conan-Doyle, 1892/
2001), edited to approximately 5,000 words. This was the
same version that had been used by Smallwood et al.
(2008).
Design
The basic rationale for the mind-wandering algorithm
was based on findings from the pilot study that when
participants are paying attention, they are slowest at
times when the text is more difficult (i.e., for many-letter,
multisyllable, low-familiarity words). Therefore, it was
during the difficult text that thought probes would be
initiated based on participants’ reaction times to the
words. At times in the story when the text was difficult
and a participant was going fast, we predicted that the
participant would be off task; if a participant was going
slowly during difficult text, we predicted that he or she
would be on task.
Text difficulty was calculated using the lexical variables
from the pilot study. Words were categorized as long (at
least four letters) or short (less than four letters). There were
approximately equal numbers of long (2,577, or 50.24%)
and short (2,552, or 49.76%) words. Words were also
categorized as having many (at least two syllables) or few
(less than two syllables) syllables. Although this measure
Psychon Bull Rev
was more biased, since there were more words with only a
few syllables (N = 4,241; 82.69%), there were still an
adequate number of words with many syllables for a
meaningful analysis (N = 888; 17.31%). The words in the
text were assigned a familiarity score using the MRC
psycholinguistic database (Coltheart, 1981; 78% of the text
words were in the database, and the missing 22% were
automatically classified as low-familiar words). The values
ranged from 100 to 700, with a mean of 488 and standard
deviation of 99. Based on the mean, words with a value less
than or equal to 488 were classified as low-familiar words
(N = 1,230; 23.98%), those words above 488 were
classified as high-familiar words (N = 3,899; 76.02%).
Using these criteria, each word was classified according to
word length (long = 1/short = 0), number of syllables
(many = 1/ few = 0), and word familiarity (low = 1, high =
0). With these categories coded numerically, each word was
assigned a mean difficulty rating ranging from 0 (easy) to 1
(hard) based on the average of the three lexical variables. A
running average consisting of 10 words was used to
provide a measure of the local difficulty of portions of
the text within the story. The mean local text difficulty
was .30 (SD = .09). On the basis of the pilot testing, we
used a threshold of .45, with text having a value greater
than this being classified as “difficult.” Therefore, thought
probes could only be initiated if the local text difficulty
was greater than .45.
In order to determine whether participants were
going fast or slow, a measure of their local reaction
time (LRT, using a running average consisting of 10
words) was compared to a measure of their global
reaction time (GRT, using a cumulative running
average). If the LRT was less than the GRT, the
participant was classified as going fast. If the LRT
was greater than the GRT, the participant was slow.
Since it was possible to further refine this speed
classification, we manipulated the amount faster or
slower that the LRT had to be relative to the GRT to
initiate a thought probe. The following values were
based on pilot data. For participants to be considered as
going fast (i.e., off task), their LRT had to be less than
0.55 times the GRT. Participants were considered as
going slow (i.e., on task) if their LRT was greater than
1.3 times the GRT and less than 1.75 times the GRT.
This second criterion was applied in order to avoid
classifying participants as being on task when they
were going extremely slow, since mind wandering is
also associated with increased reading times (Reichle et
al. 2010). A more conservative threshold was used to
initiate off-task probes because pilot data suggested that
there are more off- than on-task probes. This is likely due
to the fact that participants tend to go faster as the task
continues.
Participants who were probed used a 1–5 scale to rate
the extent to which they were focused on the task.
Specifically, for each thought probe, participants were
asked “In the moments prior to the probe, was your
attention focused: (1) Completely on the task (2) Mostly
on the task (3) On both the task and unrelated concerns
(4) Mostly on unrelated concerns (5) Completely on
unrelated concerns.” This was done in order to treat mind
wandering as a nondiscrete state and to allow us to
capture more subtle differences in the subjective reports
of mind wandering (see Christoff, Gordon, Smallwood,
Smith, & Schooler, 2009, for the use of a similar
technique).
Procedure
The text was presented word by word in black on a
white screen. Participants advanced the text by pressing
the space bar. The words remained on the screen for at
least 150 ms in order to make sure that the participants
fixated on all of the words. For participants in the probe
condition, the thought probes were presented only at
times that the algorithm predicted either on- or off-task
behavior. After participants had finished reading the text,
they answered 23 comprehension questions. Each question had four possible answers. The entire task took
approximately 50 min.
Results and discussion
Comprehension
Reading comprehension did not differ between the probed
(mean accuracy = 61.23%, SD = 0.15) and the nonprobed
(mean accuracy = 59.01%, SD = 0.15) participants. For the
probed participants, comprehension accuracy correlated
negatively with participants’ mean thought probe score;
that is, a higher score was associated with lower accuracy
(r = −.35, p < .05). In addition, comprehension accuracy
also correlated negatively with the number of predicted offtask episodes (r = −.33, p < .05). For the nonprobed
participants, comprehension accuracy also correlated negatively with the number of predicted off-task episodes
(r = −.54, p < .01). There was no significant difference
between these correlations (Fisher’s r-to-z transformation:
z = .85, p = .40).
Mind wandering based on thought probe type
The success of the algorithm was determined by comparing
the mean thought probe scores from when participants were
predicted to be either on or off task, using a repeated
Psychon Bull Rev
measures ANOVA. Given that on- and off-task probes were
only initiated if the reading behavior conformed to the
algorithm parameters, a few participants received only ontask (n = 2) or off-task (n = 2) probes. In order to include
these participants in the analysis, which captures the
effectiveness of the algorithm for participants who are
mostly on or off task, these missing values were replaced
with the mean on- or off-task thought probe score. This
analysis revealed a significant difference between the
predicted on-task thought probe score (M = 2.40, SD =
1.27) as compared to the predicted off-task thought probe
score (M = 3.33, SD = 1.32) [F(1, 27) = 6.50, p = .01, d =
0.71]. Participants, therefore, reported more mind wandering when we predicted them to be off task, and less mind
wandering when we predicted them to be on task.
In addition, it was also possible to calculate the
probability that the algorithm correctly identified whether
a person was on versus off task if these were treated as
binary categories (see, e.g., Smallwood et al. 2004). In
order to do this, given the 1–5 scale used to measure mind
wandering, all responses of 1 and 2 were coded as on task,
and all responses of 4 and 5 were coded as off task. On
average, there were 11.3 thought probes per participant for
this analysis (on task = 5.0, off task = 6.3; in the
nonprobed condition, there were comparable numbers of
thought probes: total = 10.8, on task = 6.4, off task = 4.4).
We then used an exact binomial test to compare the
success of the algorithm for each participant to what
would be expected by chance. The expected chance
probability was determined to be 49% through a Monte
Carlo simulation (10,000 iterations) that randomly recategorized the thought probes as on or off task while
maintaining the same proportion of on- versus off-task
thought probes. This expected chance value was slightly
less than 50% because, while the algorithm tends to
predict participants as being off task more often than on
task (57.2%), participants responded using 4 or 5 less
often (47.1% of the time) than 1 or 2. This analysis reveals
that the algorithm was successful at predicting whether a
participant was on versus off task 72.0% of the time (p <
.0001, 95% confidence interval 66.4%–77.0%). Of course,
72% accuracy would not be that impressive if participants
happened to be on task (or off task) a disproportionate
amount of time. Importantly, however, it was shown that
participants were about equally likely to report being on
versus off task.
Together, these results suggest that it is possible to use
behavioral measures to predict in real time when participants are mind wandering while reading. In addition, data
from the nonprobed participants show that the probes do
not fundamentally alter the nature of the reading task and,
more importantly, that the algorithm can provide a covert
measure of mind wandering.
General discussion
The results from this study show that reaction times for
advancing individual words in a word-by-word reading
paradigm can be used as an index of mind wandering and
can predict in real time whether or not a participant will
report being on or off task when given a thought probe.
These findings constitute the first demonstration that it is
possible to “catch” mind wandering in real time as it
happens. The present paradigm thus provides a critical tool
for assessing mind wandering, enabling us to precisely
assess its efficacy (by comparing mind-wandering rates
when participants are predicted to be on vs. off task).
Critically, the algorithm was capable of estimating the
accuracy of comprehension even when participants were
never probed, indicating that these results are not an artifact
of the experience-sampling methodology.
We first showed that when participants were paying
attention to the text, they were sensitive to lexical effects
and evidenced increased reaction times for long, multisyllable, low-familiar words; during mindless reading, these
effects were dampened. While these results are consistent
with work using an eyetracking methodology (Reichle et al.
2010), being able to assess mindless reading with manual
reaction times is particularly advantageous, in that any
computer could easily be used to recognize mind wandering
while reading, which currently is not feasible with eyetracking, given the high costs of the equipment.2 In
addition, this covert measure of mind wandering could
provide new insight into the processes involved in mind
wandering by avoiding the reactivity issues that potentially
accompany studies in which participants are explicitly
asked whether or not they are mind wandering.
Although the present results closely mirrored those of
Reichle et al. (2010) with respect to the relationship
between mind wandering and sensitivity to the lexical
qualities of words, they diverged with respect to the overall
reaction times preceding the thought probes. Whereas
eyetracking revealed an overall increase in gaze duration
for individual words prior to mind wandering, the present
paradigm showed the opposite results, with participants
speeding up during mind-wandering episodes. One potential explanation for this discrepancy is that there are simply
paradigmatic differences between word-by-word reading
and naturalistic reading (one page at a time), where certain
aspects of reading behavior change without necessarily
resulting in overall changes in comprehension (Just et al.
2
Given the parallels between word-by-word and eyetracking measures, it is likely that the algorithm developed here could, with modest
modifications, also be applied to eyetracking devices. If such devices
became more affordable, they would be ideal for pedagogical
purposes, since they could then monitor naturalistic reading.
Psychon Bull Rev
1982). An analogue to the speeding up of responses while
mind wandering in this word-by-word reading paradigm
may be sustained attention to response tasks (SART), in
which participants also respond frequently to individual
items appearing in the center of the screen. In the SART,
responses are similarly faster and error rates increase when
participants are mind wandering (Smallwood et al. 2004;
Smallwood et al. 2007; Smallwood et al. 2008), mirroring
the relationship between the local reaction time speed-up
and reading comprehension in the present study. An
alternative reason for the discrepancy between the eyetracking and word-by-word paradigms may entail intrinsic
differences in the relationship between information processing and hand versus eye movements; when a participant
is mind wandering, gaze duration may naturally slow due to
the inherent information extraction associated with the eyes,
whereas finger tapping may naturally speed up due to the
common link between automatized behaviors and rapid
hand movements.
Future work, varying both mode of advancing the text
(i.e., via eyes vs. via the hand) and the presentation type
(e.g., single words vs. sentences) could offer further insight
into this issue. If, for example, eyetracking during word-byword reading still leads to longer gaze durations while mind
wandering, this would support the information extraction
hypothesis proposed above. If, however, mind wandering
was associated with shortened gaze durations during wordby-word reading (vs. naturalistic reading), and/or increased
reaction times when reading text a sentence at a time (or a
page at time), this would indicate that the presentation style
is responsible for this discrepancy. Despite these differences, the fact that mind wandering systematically alters
reading behavior in both word-by-word reading and
naturalistic reading suggests that the present algorithm
could be tailored to either context.
The most significant aspect of this study is that it is the
first demonstration of a capacity to predict mind wandering
in real time. In order to better understand the basic rationale
of the algorithm used, consider the analogy of trying to
catch a person speeding while driving. Since it is not
feasible to have officers stationed at every possible
location, only certain locations are chosen—for instance,
the dreaded school zone coming after a 55-mph stretch of
road. Likewise, in our study the sensitivity of the algorithm
for catching mind wandering was maximized by assessing
participants’ attention at difficult points in the text, where,
as in the comparable scenario of the school zone,
participants might not slow down if they were failing to
pay attention. These “speed traps” were successful in
discriminating on- versus off-task behavior because participants who were mind wandering tended to go fast at these
difficult points in the text, while participants who were
paying attention tended to slow down.
These results are important from a pedagogical
perspective, because they suggest that it may be possible
to decrease the amount of mind wandering that readers
engage in by cuing them to reengage the text when their
reading behavior suggests they are mind wandering.
Given the consequences of mind wandering on comprehension demonstrated here and in prior work (Schooler et
al. 2004; Smallwood et al. 2008), it is likely that an
intervention that was able to decrease mind wandering
could have a significant impact on students’ ability to
understand the text that they are reading. For example,
students could first be assessed for their tendency to mind
wander with a random-probe word-by-word reading
paradigm; they could then receive practice with the
present paradigm, whereby participants receive regular
feedback regarding precisely when they are mind wandering. This feedback might provide a critical training tool for
enhancing individuals’ ability to catch mind-wandering
episodes for themselves, thereby promoting a metaawareness of mind wandering (Sayette, Reichle, &
Schooler, 2009; Schooler, 2002) that would enable it to
be kept in check. Success could be assessed by measuring
comprehension accuracy and the number of predicted offtask episodes in a no-probe version. Given that the present
algorithm required extensive analysis of the text’s lexical
features, future work with different texts will be needed in
order to see how well the present parameters in the
algorithm generalize to other text. For example, it may be
possible with mostly high-familiar text (such as in children’s
stories) to only use the number of syllables and the number of
letters for a given word and still to successfully predict mind
wandering.
Finally, the present approach may help overcome many of
the experimental challenges to mind-wandering research. An
important contribution of the present experiments is that they
demonstrate that subtle changes in behavior that are associated with mind wandering can provide a more nuanced
measure of mind wandering. One advantage is that by
bypassing the requirement for self-report, it is possible to
assess the extent to which introspection might fundamentally
alter participants’ behavior, their underlying mental states,
their neurocognitive activity, and/or the relationship between
these components (Schooler, 2002). A key advance of the
research presented in this article is that the question of
whether thought probes have a reactive consequence is now
an experimental question. Likewise, a limit in the
experience-sampling method is that the number of probes
provides an upper limit on the number of times that mind
wandering can be reported. By contrast, in the present
approach, the limit in the frequency that probes occur is
related to the frequency of the “speed traps”/difficult text
(which can vary with much greater freedom). The capability
of the present algorithm to covertly track mind-wandering
Psychon Bull Rev
episodes in real time in the future will allow a more powerful
estimate of the occurrence of this state. Finally, the fact that
the present procedure allows mind wandering to be assessed
without recourse to self-report allows for the extension of the
mind-wandering procedure to groups such as children, as
well as to populations with language problems (such as
autism), for whom the veracity of self-reported information
may be doubted.
Author Note We thank Erik Reichle for his contributions to the
manuscript. The work reported in this article was supported by the
Bower Foundation and the Institute of Educational Sciences
(R305A110277). The opinions expressed are those of the authors
and do not represent views of the Institute or the U.S. Department of
Education.
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